Research Library
The research behind practical private AI
We build on peer-reviewed work, not hype. This is a curated, searchable library of the papers that shape how we deploy Small Language Models, retrieval, and automation — readable here, in full, straight from arXiv.
Featured: “Small Language Models are the Future of Agentic AI” — NVIDIA Research.
Why this matters
Decisions grounded in evidence, not trends.
The fastest way to waste money on AI is to follow fashion. The techniques below are what let organizations deploy AI that is secure, affordable, and genuinely useful — and they inform every engagement we take on.
Right-sized models
Evidence that small, specialized models can match larger ones on focused tasks — at a fraction of the cost and latency.
Grounded knowledge
Retrieval techniques that let models answer from your controlled sources instead of memorized, unverifiable weights.
Efficient adaptation
Fine-tuning and distillation methods that make domain customization practical on infrastructure you govern.
Library
Browse & read.
Pick a paper from the list to read its full PDF in the reader. Use the panel toggle to collapse the list and give the document more room. Search by title, author, topic, or arXiv ID.
Agents & Automation · 2025
Small Language Models are the Future of Agentic AI
Belcak, Heinrich, Diao, Molchanov, et al. · NVIDIA Research
A position paper arguing that small, specialized models are powerful enough, more suitable, and far more economical than large general models for the repetitive tasks agents actually perform. Includes a practical LLM-to-SLM conversion algorithm.
PDF served live from arXiv.org. If it doesn't load, open it in a new tab.
Apply the Research
Want this applied to your workflows?
We translate the research above into secure, right-sized systems for your business. Start with a focused assessment.